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0-Architecture.md

0-Architecture.md

 
 

1-Challenge.md

1-Challenge.md

 
 

10-NLP in Vertical Area.md

10-NLP in Vertical Area.md

 
 

11-NLP应用场景及挑战.md

11-NLP应用场景及挑战.md

 
 

12-NLP Tools.md

12-NLP Tools.md

 
 

13-AI平台.md

13-AI平台.md

 
 

14-Current Company of NLP.md

14-Current Company of NLP.md

 
 

Adversarial Learning.md

Adversarial Learning.md

 
 

Attention Summary.md

Attention Summary.md

 
 

Attention.md

Attention.md

 
 

Bayesian Program Learning.md

Bayesian Program Learning.md

 
 

Code Generation.md

Code Generation.md

 
 

Combine neural and symbolic.md

Combine neural and symbolic.md

 
 

Commonsense Inference.md

Commonsense Inference.md

 
 

Favorite NLP quotes.md

Favorite NLP quotes.md

 
 

Few Short Learning Summary.md

Few Short Learning Summary.md

 
 

Few Shot Learning.md

Few Shot Learning.md

 
 

Keyphrase Generation.md

Keyphrase Generation.md

 
 

Label Limited learning.md

Label Limited learning.md

 
 

Label Noise Learning.md

Label Noise Learning.md

 
 

Long tailed problem.md

Long tailed problem.md

 
 

Machine Translation - alignment.md

Machine Translation - alignment.md

 
 

Machine Translation Challenge.md

Machine Translation Challenge.md

 
 

Machine Translation Summary.md

Machine Translation Summary.md

 
 

Machine Translation.md

Machine Translation.md

 
 

Natural Language Generation Summary.md

Natural Language Generation Summary.md

 
 

Natural Language Reasoning.md

Natural Language Reasoning.md

 
 

Natural Language Understanding.md

Natural Language Understanding.md

 
 

Natural language Generation.md

Natural language Generation.md

 
 

Paraphrase Generation.md

Paraphrase Generation.md

 
 

Poetry Generation.md

Poetry Generation.md

 
 

Problem.md

Problem.md

 
 

README.md

README.md

 
 

Reading Comprehension.md

Reading Comprehension.md

 
 

SQL Parsing Application.md

SQL Parsing Application.md

 
 

SQL Parsing.md

SQL Parsing.md

 
 

Semantic Parsing.md

Semantic Parsing.md

 
 

Semi-supervised Learning in NLP.md

Semi-supervised Learning in NLP.md

 
 

Sentiment Analysis Summary.md

Sentiment Analysis Summary.md

 
 

Sentiment Analysis.md

Sentiment Analysis.md

 
 

Seq2Seq.md

Seq2Seq.md

 
 

Short Text.md

Short Text.md

 
 

Spoken language understanding.md

Spoken language understanding.md

 
 

Stance Detection.md

Stance Detection.md

 
 

Story Generation.md

Story Generation.md

 
 

Style Transfer.md

Style Transfer.md

 
 

Summarization Summary.md

Summarization Summary.md

 
 

Summarization.md

Summarization.md

 
 

Text Detector Experience.md

Text Detector Experience.md

 
 

Text Match - similarity.md

Text Match - similarity.md

 
 

Text Match Summary.md

Text Match Summary.md

 
 

Text Match.md

Text Match.md

 
 

Textual Entailment.md

Textual Entailment.md

 
 

Transfer Learning Summary.md

Transfer Learning Summary.md

 
 

Transfer Learning-knowledge distilling.md

Transfer Learning-knowledge distilling.md

 
 

Transfer Learning.md

Transfer Learning.md

 
 

Unsupervised Learning in NLP.md

Unsupervised Learning in NLP.md

 
 

Zero Short Learning.md

Zero Short Learning.md

 
 

automated essay scoring.md

automated essay scoring.md

 
 

data - augmentation.md

data - augmentation.md

 
 

data - candidate sampling.md

data - candidate sampling.md

 
 

data - construction (labeling).md

data - construction (labeling).md

 
 

data - negative sampling.md

data - negative sampling.md

 
 

data - noise.md

data - noise.md

 
 

data - sample imbalance.md

data - sample imbalance.md

 
 

data - sample influence.md

data - sample influence.md

 
 

data - selection bias.md

data - selection bias.md

 
 

multi-task learning application.md

multi-task learning application.md

 
 

multi-task learning.md

multi-task learning.md

 
 

practical method for OOV problem.md

practical method for OOV problem.md

 
 

question generation.md

question generation.md

 
 

中文分词.md

中文分词.md

 
 

中文纠错-文本校对.md

中文纠错-文本校对.md

 
 

中英文NLP异同.md

中英文NLP异同.md

 
 

公告信息抽取.md

公告信息抽取.md

 
 

文本溯源（查重）.md

文本溯源（查重）.md

 
 

文档信息抽取.md

文档信息抽取.md

 
 

新词发现.md

新词发现.md

 
 

智能写作.md

智能写作.md

 
 

概率语法图模型.md

概率语法图模型.md

 
 

Repository files navigation

论文入选要求：
1. 好的方法 - 开创性或突破性方法
2. 好的应用 - 提供解决问题的全新视角

google scholar

Topics

Sentence Summarization

sentence compression(句子压缩)

(Application: 商品标题压缩，资讯标题改写，PUSH消息改写...)

Zero Pronoun Resolution

Sentimental Analysis

情感推理、偏见/观点差异检测

VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

Bias Statement Detector (BSD) computationally detects and quantifies the degree of bias in sentence-level text of news stories.

Challenges

Context-aware(Prior) and World Knowledge

Grammar

Linguistically/Semantically

WordNet

Language Model

Word Embedding

Knowledge Graph

Data Scarcity

Representation Learning

Transfer Learning

Natural Language Inference (Reasoning)

Natural Language Generation

Machine Translation

Fluency and Coherency

Methods

Graph based

Transition based

Deep Reinforcement Learning

Deep Reinforcement Learning for NLP ACL 2018 ppt

Trends

2017 - ...
2018 - 神经网络、注意机制、表示学习、语义和知识...

  • 表示学习

    1. 词汇表示学习

      融合多种信息的、任务或领域特异的、跨语言、消歧的词汇表示学习

    2. 句子表示学习

  • ...

  • The most important NLP highlights of 2018 (PDF Report)

    https://github.com/omarsar/nlp_highlights

Progress

The progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
https://github.com/sebastianruder/NLP-progress

https://github.com/Yale-LILY/Awesome-NLP-Research

https://github.com/Kyubyong/nlp_tasks

https://github.com/omarsar/nlp_overview https://nlpoverview.com/

https://aclweb.org/aclwiki/State_of_the_art

https://sotabench.com/

TutorialBank: Learning NLP Made Easier search engine

LectureBank: a corpus for NLP Education and Prerequisite Chain Learning

RoadMap & Tutorial

Conference

Competition

CoNLL

CoNLL 评测主要是学术界主导，所以内容多偏向自然语言处理的基础研究问题。

http://universaldependencies.org/conll17/
http://universaldependencies.org/conll18/

CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
result | paper

CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
result | paper

HIT-SCIR (Harbin) - Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation

Uppsala (Uppsala) - Universal Word Segmentation: Implementation and Interpretation code

Literature Review

  • Recent Trends in Deep Learning Based Natural Language Processing, Tom Young, Devamanyu Hazarika, Soujanya Poria, Erik Cambria, last revised 25 Nov 2018. arxiv

  • Analysis Methods in Neural Language Processing: A Survey, Yonatan Belinkov, James Glass, 2019 TACL. arxiv | code

    • Lexical and Neural Networks Combined
    • Adversarial Learning

Paper notes

https://github.com/papers-we-love/papers-we-love

Papers with Code

定期论文阅读

论文阅读

问题讨论

nlp-reading-group

机构：

stanford reading group

yale nlp reading group

VLG papers_we_read

个人：

NLP Tasks

papernote

丕子 paperlist

NLP-Papers

zhpmatrix PaperReading

Paper

COLING - 欧洲 - 关注语言规律、模型分析（可解释性研究）

ACL - 北美

NAACL - 北美

EMNLP -

2018 - Highlights

chinese

  • Shallow Semantic Parsing of Chinese, HLT-NAACL, 2004, Sun, Honglin，Jurafsky, Daniel paper
  • Chinese Word Segmentation: Another Decade Review (2007-2017), arxiv
  • Analogical Reasoning on Chinese Morphological and Semantic Relations, ACL 2018. paper | code

pre-training word embedding

  • Improving language understanding with unsupervised learning, OpenAI 2018

    • language model

  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, Google 2018

    • language model

pronoun resolution

  • Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution, Ting Liu, Yiming Cui, et al. , ACL2017 arxiv | slides | Yiming Cui

    • data scarcity
    • transfer learning

entity extraction

  • Unsupervised Named-Entity Extraction from the Web: An Experimental Study, 2005 paper

entity classification

  • Unsupervised Models for Named Entity Classification, Michael Collins and Yoram Singer, 1999 paper ⭐⭐⭐⭐

sentence compression

  • Global inference for sentence compression: An integer linear programming approach, J Clarke, M Lapata - Journal of Artificial Intelligence Research, 2008 ⭐⭐⭐
  • Language as a Latent Variable: Discrete Generative Models for Sentence Compression, Yishu Miao, Phil Blunsom, EMNLP 2016 arxiv
  • Sentence Compression as Tree Transduction, Trevor Anthony Cohn, Mirella Lapata, 2009 arxiv
  • Global Inference for Sentence Compression: An Integer Linear Programming Approach James Clarke，Mirella Lapata 2008 code
  • Sentence Reduction for Automatic Text Summarization, Hongyan Jing 2000 paper
  • A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data, Jingang Wang, Junfeng Tian, Long Qiu, Sheng Li, Jun Lang, Luo Si, Man Lan, AAAI 2018 arxiv

sentence summarization

  • A Neural Attention Model for Abstractive Sentence Summarization, Alexander M. Rush, Sumit Chopra, Jason Weston, Facebook, EMNLP 2015 code

natural language inference

text match/paraphrase identification/text entailment

  • Enhanced LSTM for Natural Language Inference, Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, Diana Inkpen. ACL (2017) arxiv | code
  • A Decomposable Attention Model for Natural Language Inference, Ankur P. Parikh, Oscar Täckström, Dipanjan Das, Jakob Uszkoreit, EMNLP 2016 arxiv
  • Learning Natural Language Inference with LSTM, Shuohang Wang, Jing Jiang, 2016, arxiv | code
  • Convolutional neural network architectures for matching natural language sentences, B Hu, Z Lu, H Li, Q Chen - Advances in neural information processing systems, 2014 paper

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